A number of industries today commonly integrate a high level of automation into their manufacturing processes. Such automation is capable of increasing quality and reducing the lead time required to produce many modern products such as electronic devices. Machines and electronically controlled robots are capable of tasks that require great accuracy which in many cases humans cannot do well. Examples of such tasks are placing integrated circuits on circuit boards at a specification plus or minus 1 millimeter which, for all practical purposes, is impossible for humans to perform. Robots in particular are able to perform tasks that humans do not want to do or that are dangerous to humans, and in many cases can perform these functions more efficiently.
Many of these tasks are relatively simple so that robots can be programmed or "taught" easily. Yet the problem with these types of robots is their lack of intelligence and high cost. Without training or programming, these robots arc of little use. In addition, a programmer is needed to communicate with the robot whenever changes need to be made to its functions. While the robots found in the prior art find their place in many manufacturing settings they are not practical in all environments. The poultry industry, for example, has many different sizes of chicken pieces ranging from the whole chicken to chicken legs and breast fillets. Not only are these pieces not of a prescribed dimension, but they are deformable, wet and could be easily damaged by prior art robots. Specialized concerns of the poultry industry are that robots do not harm the product and that the robots be able to survive in the rugged environment typically found in the poultry industry and that they be capable of satisfactory cleaning to comply with USDA health standards.
An additional drawback with most robots found in the prior art and used in a number of industrial manufacturing settings, is the immense capital investment necessary to finance such equipment. The poultry industry, on the other hand, is particularly concerned with a quicker payback period, typically one to two years which has led to the demand for a low cost, flexible robot that can pick-and-place a variety of poultry parts while surviving in the harsh environments.
A more flexible pick-and-place robot could be utilized by the poultry industry to gain competitive advantages. Some of the tasks required are pick-and-place operations easily accomplished by humans. While the poultry industry currently has a great deal of fixed automation, people still must perform a number of repetitive tasks that are currently difficult for robots to perform. It would be advantageous to use robotics because they can perform tasks more consistently and function 24 hours a day. Also, the cost of operating a robot is much less expensive in the long term. For these reasons, a demand would exist in the poultry industry if a robot could manipulate slippery, different-sized, deformable pieces of product at an affordable price installation and maintenance.
To perform pick-and-place operations of individual poultry parts, a robot must have an end effector or hand that is capable of grasping a variety of parts. The Intelligent Machines Branch of the Georgia Tech Research Institute has developed the first human level performance robot called the Intelligent Integrated Belt Manipulator. This robot is a three degree of freedom, pick-and-place robot which interfaces with a conveyor. The end effector models the grasping of the human hand which allows a limited tolerance or error in the determination of the center of gravity of the part that is to be picked up.
While pick-and-place robots are capable of handling parts, such as pieces of poultry, the robots must be told the location of the part in relation to itself and exactly where to go to pick up the part. Several methods in the prior art have been used to determine the location of parts on conveyors. Machine vision has frequently been used for this purpose, which can be done in both two and three dimensions. Machine vision is typically implemented using a black and white camera to capture images of the parts on a conveyor. A predetermined algorithm can then be used to calculate centroid and major axis from the pixels of the image. To fully implement such a machine vision system, the following equipment and software is necessary: a gray scale camera, substantial lighting equipment, an image digitizer and processor, software which calculates the centroid and major axis of individual images and software to interface the machine vision system to the pick-and-place robot's control system. The described machine vision system has the advantage of excellent accuracy in that the centroid can be computed to within one pixel. For a camera with 256 by 256 resolution and a field of view of three feet by three feet, the dimension of a pixel is a square of 0.14 inches per side. This greatly exceeds the accuracy needed for use with objects such as pieces of poultry. Despite its accuracy, there are numerous disadvantages to machine vision tracking which include a high expenditure for vision hardware and lighting, sensitive equipment not capable of withstanding harsh environments found in poultry processing plants, the inability to wash the machine vision hardware effectively and a delay of close to half a second to capture images before processing them.
An alternate approach to part location used in the prior art employs photoelectric sensors. These sensors can be used for parts of known dimensions and specific orientations to determine part location coordinates. In one known application photoelectric sensors are used to determine if broken bottles exist in a beverage processing line. If a broken bottle is detected, a plunger pushes the case of bottles off the processing line for reprocessing. This particular example is a one dimensional tracking system.